






"""simple docstring"""





import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME







def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							str:
  lowerCamelCase_       	=		[
      'encoder.version',
      'decoder.version',
      'model.encoder.version',
      'model.decoder.version',
      'decoder.output_projection.weight',
      '_float_tensor',
      'encoder.embed_positions._float_tensor',
      'decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    state_dict.pop(_lowerCamelCase						,							_lowerCamelCase					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple					) ->							Optional[int]:
  lowerCamelCase_	,      lowerCamelCase_       	=		emb.weight.shape
  lowerCamelCase_       	=		nn.Linear(_lowerCamelCase						,							_lowerCamelCase						,							bias=_lowerCamelCase					)
  lowerCamelCase_       	=		emb.weight.data
  return lin_layer







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						Optional[Any]=None					) ->							Optional[int]:
  lowerCamelCase_       	=		{}
  for old_key in state_dict.keys():
    lowerCamelCase_       	=		old_key
    if "moe_layer.experts." in key:
      if expert_idx is not None:
        lowerCamelCase_       	=		key.replace('moe_layer.experts.0'						,							F'''ffn.experts.expert_{expert_idx}'''					)
      else:
        lowerCamelCase_       	=		key.replace('moe_layer.experts.'						,							'ffn.experts.expert_'					)
    if "gate" in key:
      lowerCamelCase_       	=		key.replace('.moe_layer.gate.wg'						,							'.ffn.router.classifier'					)
    if "fc2" and "experts" not in key:
      lowerCamelCase_       	=		key.replace('.fc2.'						,							'.ffn.fc2.'					)
    if "fc1" and "experts" not in key:
      lowerCamelCase_       	=		key.replace('.fc1.'						,							'.ffn.fc1.'					)
    if ".encoder_attn." in key:
      lowerCamelCase_       	=		key.replace('.encoder_attn.'						,							'.cross_attention.'					)
    if "encoder_attn_layer_norm" in key:
      lowerCamelCase_       	=		key.replace('encoder_attn_layer_norm'						,							'cross_attention_layer_norm'					)
    if "final_layer_norm" in key:
      lowerCamelCase_       	=		key.replace('final_layer_norm'						,							'ff_layer_norm'					)
    lowerCamelCase_       	=		state_dict[old_key]
  return new_dict







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any						,							_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						int						,							_lowerCamelCase			:						str = WEIGHTS_NAME					) ->							Union[str, Any]:
  lowerCamelCase_       	=		[]
  lowerCamelCase_       	=		0
  os.makedirs(_lowerCamelCase						,							exist_ok=_lowerCamelCase					)

  for expert in range(_lowerCamelCase					):
    lowerCamelCase_       	=		switch_checkpoint_path + F'''-rank-{expert}.pt'''
    if os.path.isfile(_lowerCamelCase					):
      lowerCamelCase_       	=		torch.load(_lowerCamelCase					)['model']
      remove_ignore_keys_(_lowerCamelCase					)
      lowerCamelCase_       	=		rename_fairseq_keys(_lowerCamelCase						,							_lowerCamelCase					)
      lowerCamelCase_       	=		os.path.join(
          _lowerCamelCase						,							weights_name.replace('.bin'						,							F'''-{len(_lowerCamelCase					)+1:05d}-of-???.bin'''					)					)
      torch.save(_lowerCamelCase						,							_lowerCamelCase					)
      sharded_state_dicts.append(expert_state.keys()					)
      total_size += sum([value.numel() for key, value in expert_state.items()]					) * dtype_byte_size(
          expert_state[list(_lowerCamelCase					)[0]].dtype					)

    # Add the last block
  lowerCamelCase_       	=		os.path.join(_lowerCamelCase						,							weights_name.replace('.bin'						,							F'''-{len(_lowerCamelCase					)+1:05d}-of-???.bin'''					)					)
  lowerCamelCase_       	=		torch.load(switch_checkpoint_path + '-shared.pt'					)['model']
  remove_ignore_keys_(_lowerCamelCase					)
  lowerCamelCase_       	=		rename_fairseq_keys(_lowerCamelCase						,							_lowerCamelCase					)
  lowerCamelCase_       	=		shared_weights['decoder.embed_tokens.weight']
  sharded_state_dicts.append(shared_weights.keys()					)

  # If we only have the shared weights (dummy model/experts saved on the same file)
  if len(_lowerCamelCase					) == 1:
    lowerCamelCase_       	=		os.path.join(_lowerCamelCase						,							_lowerCamelCase					)
    torch.save(_lowerCamelCase						,							_lowerCamelCase					)
    return {weights_name: sharded_state_dicts[0]}, None
  else:
    torch.save(_lowerCamelCase						,							_lowerCamelCase					)
  # Otherwise, let's build the index
  lowerCamelCase_       	=		{}
  for idx, shard in enumerate(_lowerCamelCase					):
    lowerCamelCase_       	=		weights_name.replace('.bin'						,							F'''-{idx+1:05d}-of-{len(_lowerCamelCase					):05d}.bin'''					)
    lowerCamelCase_       	=		os.path.join(_lowerCamelCase						,							weights_name.replace('.bin'						,							F'''-{idx+1:05d}-of-???.bin'''					)					)
    os.rename(_lowerCamelCase						,							os.path.join(_lowerCamelCase						,							_lowerCamelCase					)					)
    for key in shard:
      lowerCamelCase_       	=		shard_file

    # Add the metadata
  lowerCamelCase_       	=		{'total_size': total_size}
  lowerCamelCase_       	=		{'metadata': metadata, 'weight_map': weight_map}

  with open(os.path.join(_lowerCamelCase						,							_lowerCamelCase					)						,							'w'						,							encoding='utf-8'					) as f:
    lowerCamelCase_       	=		json.dumps(_lowerCamelCase						,							indent=2						,							sort_keys=_lowerCamelCase					) + '\n'
    f.write(_lowerCamelCase					)

  return metadata, index


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							Optional[int]          =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--nllb_moe_checkpoint_path''',
      default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000''',
      type=str,
      required=False,
      help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
  )
  parser.add_argument('''--dtype''', default='''float32''', type=str, required=False, help='''dtype of the saved model''')
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b''',
      type=str,
      required=False,
      help='''Path to the output pytorch model.''',
  )
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		parser.parse_args()
  _SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							List[str]          =		shard_on_the_fly(
      args.nllb_moe_checkpoint_path,
      args.pytorch_dump_folder_path,
      128,
      args.dtype,
  )

  _SCREAMING_SNAKE_CASE   :							str          =		NllbMoeConfig.from_pretrained(
      '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
  )
  config.save_pretrained(args.pytorch_dump_folder_path)
  _SCREAMING_SNAKE_CASE   :							Any          =		NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
  print('''Done''')
  model.save_pretrained(args.pytorch_dump_folder_path)












"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						int = 10						,							_lowerCamelCase			:						int = 1000						,							_lowerCamelCase			:						bool = True					) ->							int:
  assert (
      isinstance(_lowerCamelCase						,							_lowerCamelCase					)
      and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
      and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  ), "Invalid type of value(s) specified to function!"

  if min_val > max_val:
    raise ValueError('Invalid value for min_val or max_val (min_value < max_value)'					)
  return min_val if option else max_val







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						int					) ->							int:
  return int((number_a + number_a) / 2					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						int						,							_lowerCamelCase			:						int					) ->							None:
  assert (
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and isinstance(_lowerCamelCase						,							_lowerCamelCase					) and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  ), 'argument values must be type of "int"'

  if lower > higher:
    raise ValueError('argument value for lower and higher must be(lower > higher)'					)

  if not lower < to_guess < higher:
    raise ValueError(
        'guess value must be within the range of lower and higher value'					)

  def answer(_lowerCamelCase			:						int					) -> str:
    if number > to_guess:
      return "high"
    elif number < to_guess:
      return "low"
    else:
      return "same"

  print('started...'					)

  lowerCamelCase_       	=		lower
  lowerCamelCase_       	=		higher

  lowerCamelCase_       	=		[]

  while True:
    lowerCamelCase_       	=		get_avg(_lowerCamelCase						,							_lowerCamelCase					)
    last_numbers.append(_lowerCamelCase					)

    if answer(_lowerCamelCase					) == "low":
      lowerCamelCase_       	=		number
    elif answer(_lowerCamelCase					) == "high":
      lowerCamelCase_       	=		number
    else:
      break

  print(F'''guess the number : {last_numbers[-1]}'''					)
  print(F'''details : {last_numbers!s}'''					)







def 	lowerCamelCase__			(				) ->							None:
  lowerCamelCase_       	=		int(input('Enter lower value : '					).strip()					)
  lowerCamelCase_       	=		int(input('Enter high value : '					).strip()					)
  lowerCamelCase_       	=		int(input('Enter value to guess : '					).strip()					)
  guess_the_number(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)


if __name__ == "__main__":
  main()




